Infrastructure Validation Engineer in London

Infrastructure Validation Engineer in London

London Full-Time 63000 - 77000 £ / year (est.) On-site
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At a Glance

  • Tasks: Design and build automated validation tools for cutting-edge AI infrastructure.
  • Company: Join a mission-driven start-up redefining enterprise infrastructure.
  • Benefits: Competitive salary, autonomy, and opportunities for growth in a dynamic environment.
  • Other info: Diverse and inclusive workplace with high visibility and leadership opportunities.
  • Why this job: Make a real impact on next-gen AI infrastructure and work with innovative technologies.
  • Qualifications: Strong Python skills and experience with Kubernetes and Infrastructure as Code.

The predicted salary is between 63000 - 77000 £ per year.

We are seeking an AI Infrastructure Validation Engineer to join our fast-scaling team. This role sits within Product but works across Product, Engineering and Operations. You will design, build, and orchestrate the automated preflight validation suites and performance benchmarks that continuously verify our bare-metal APIs, Kubernetes environments, and multi-node GPU clusters under enterprise-scale workloads. You will ensure that every platform release, infrastructure change or hardware deployment is tested, validated and production-ready before reaching customers. This is an opportunity to join a mission-led AI business that is redefining infrastructure, intelligence, and impact for enterprise customers.

Key Responsibilities:

  • Software-Defined Infrastructure Validation & Preflight Automation:
    • Design and build zero-dependency, Python-based “preflight” verification tools to validate multi-node distributed initialization, master-to-worker rendezvous routing, and correct GPU-to-CPU process affinity prior to launching massive model‑training workloads.
    • Write and maintain Infrastructure as Code to provision, configure, test, and teardown complex bare-metal and containerized compute environments.
    • Implement Resilient Execution: Construct adaptive, intelligent test orchestration harnesses that can autonomously detect environment drifts and analyse platform changes.
  • GPU Platform & Low-Latency Network Validation:
    • Automate the execution and results aggregation of cluster-level benchmarking suites and high-performance storage benchmarks to validate node-to-node throughput limits.
    • Build validation routines to monitor high-throughput network fabrics, evaluating traffic patterns, congestion control parameters.
    • Script low-level automated checks to validate server-node topology, PCIe link speeds, HBM memory status, secure boot parameters, and firmware performance via BMC, IPMI, or Redfish interfaces.
  • Continuous Integration & Observability:
    • Integrate automated infrastructure validation suites directly into CI/CD pipelines.
    • Configure and maintain observability pipelines to route real-time diagnostic logs and hardware execution metrics to quickly isolate slow, misconfigured, or degrading compute nodes.
    • Partner with the Platform team, Network Engineers, and Datacentre Operations to lead root‑cause analysis on complex platform regressions, hardware‑software boundaries, and distributed interconnect bottlenecks.

Essential Experience:

  • Strong proficiency in Python for building zero-dependency verification tools, automated test orchestration harnesses, and low-level system checks.
  • Deep hands‑on experience writing and maintaining Infrastructure as Code to provision, configure, and teardown complex bare‑metal and containerized compute environments.
  • Proven experience working within Kubernetes environments and validating enterprise‑scale, multi‑node distributed systems.
  • Scripting automated checks for server‑node topology, PCIe link speeds, HBM memory status, firmware performance, and interfacing with hardware via BMC, IPMI, or Redfish.
  • Demonstrated capability integrating automated infrastructure validation suites into CI/CD pipelines.
  • Configuring observability pipelines for real‑time diagnostic logs and hardware metrics.
  • History of partnering across Engineering, Product, and Datacentre Operations to conduct root‑cause analysis on complex platform regressions and hardware‑software boundaries.

Preferred Experience:

  • Prior experience validating infrastructure specifically optimized for massive model‑training workloads, including a solid understanding of GPU‑to‑CPU process affinity and master‑to‑worker rendezvous routing.
  • Deep understanding of high-throughput network fabrics, traffic patterns, and congestion control parameters in multi‑node GPU clusters.
  • Background in building and executing cluster‑level benchmarking suites and high-performance storage benchmarks to isolate node-to-node throughput limits.
  • Experience designing intelligent test systems capable of autonomously detecting environment drifts and analysing large‑scale platform changes.

Why Join Era4:

You’ll be joining a mission‑driven start‑up building critical national infrastructure, where operational excellence directly enables growth. This role offers high visibility with leadership, real autonomy, and the chance to shape how a next‑generation company operates at scale.

Diversity & Inclusion:

Era4 is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Infrastructure Validation Engineer in London employer: Carbon3.ai

At Era4, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Security Engineer, you will have the opportunity to work at the forefront of AI technology in a dynamic environment, with ample opportunities for professional growth and development. Our commitment to employee well-being is reflected in our comprehensive benefits package and a supportive atmosphere that encourages creativity and teamwork.

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Contact Details:

Carbon3.ai Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Infrastructure Validation Engineer in London

✨Join Local Tech Meetups

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✨Contribute to Open Source Projects

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We think you need these skills to ace Infrastructure Validation Engineer in London

Python
Infrastructure as Code
Kubernetes
Automated Testing
CI/CD Pipelines
Observability Pipelines
Root Cause Analysis

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Carbon3.ai.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Carbon3.ai and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Carbon3.ai

✨Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

✨Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Carbon3.ai uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

✨Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

✨Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.